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International Journal of Applied &... 2024Supraglottic devices have revolutionized the current practice of airway management. We compared the clinical performance of a recently introduced BlockBuster™...
BACKGROUND
Supraglottic devices have revolutionized the current practice of airway management. We compared the clinical performance of a recently introduced BlockBuster™ Laryngeal mask airway with i-gel in adult patients under general anesthesia.
METHODS
Following Institutional ethical clearance, the present study was conducted on 62 patients belonging to American Society of Anesthesiologists physical status 1 and 2 of either sex in the age group of 20-60 years under general anesthesia. Patients were randomly assigned to i-gel (I) and BlockBuster™ (B) groups (31 per group). Time for successful insertion, insertion success rate, ease of insertion, oropharyngeal leak pressures (OLPs), and complications were assessed.
RESULTS
Mean insertion time of device was less in Group I (13.52 ± 2.58 s) than that of Group B (14.10 ± 2.04 s), which was neither clinically nor statistically significant ( = 0.330). OLP in Group B (24.52 ± 2.77 cm of H2O) was found to be significantly higher compared to Group I (20.81 ± 2.56 cm of H2O) with < 0.001. Overall insertion and first attempt success was similar (i-gel 31/31 [100%] and 29/31 [93.5%] and BlockBuster™ 31/31 [100%] and 29/31 [93.5%], respectively). Ease of insertion ( = 0.684) and complications ( = 0.782) of both the devices were comparable.
CONCLUSIONS
Both the devices are useful and effective for airway management in adult under general anesthesia. Having a high OLP and comparable insertion time, risk of aspiration may be further reduced with the use of BlockBuster™ in comparison to i-gel.
PubMed: 38912364
DOI: 10.4103/ijabmr.ijabmr_520_23 -
GeoHealth Jun 2024Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to...
Many infectious disease forecasting models in the United States (US) are built with data partitioned into geopolitical regions centered on human activity as opposed to regions defined by natural ecosystems; although useful for data collection and intervention, this has the potential to mask biological relationships between the environment and disease. We explored this concept by analyzing the correlations between climate and West Nile virus (WNV) case data aggregated to geopolitical and ecological regions. We compared correlations between minimum, maximum, and mean annual temperature; precipitation; and annual WNV neuroinvasive disease (WNND) case data from 2005 to 2019 when partitioned into (a) climate regions defined by the National Oceanic and Atmospheric Administration (NOAA) and (b) Level I ecoregions defined by the Environmental Protection Agency (EPA). We found that correlations between climate and WNND in NOAA climate regions and EPA ecoregions were often contradictory in both direction and magnitude, with EPA ecoregions more often supporting previously established biological hypotheses and environmental dynamics underlying vector-borne disease transmission. Using ecological regions to examine the relationships between climate and disease cases can enhance the predictive power of forecasts at various scales, motivating a conceptual shift in large-scale analyses from geopolitical frameworks to more ecologically meaningful regions.
PubMed: 38912225
DOI: 10.1029/2024GH001024 -
Medical Research Archives May 2024Respiratory fluid dynamics is integral to comprehending the transmission of infectious diseases and the effectiveness of interventions such as face masks and social...
On the efficacy of facial masks to suppress the spreading of pathogen-carrying saliva particles during human respiratory events: Insights gained via high-fidelity numerical modeling.
Respiratory fluid dynamics is integral to comprehending the transmission of infectious diseases and the effectiveness of interventions such as face masks and social distancing. In this research, we present our recent studies that investigate respiratory particle transport via high-fidelity large eddy simulation coupled with the Lagrangian particle tracking method. Based on our numerical simulation results for human respiratory events with and without face masks, we demonstrate that facial masks could significantly suppress particle spreading. The studied respiratory events include coughing and normal breathing through mouth and nose. Using the Lagrangian particle tracking simulation results, we elucidated the transport pathways of saliva particles during inhalation and exhalation of breathing cycles, contributing to our understanding of respiratory physiology and potential disease transmission routes. Our findings underscore the importance of respiratory fluid dynamics research in informing public health strategies to reduce the spread of respiratory infections. Combining advanced mathematical modeling techniques with experimental data will help future research on airborne disease transmission dynamics and the effectiveness of preventive measures such as face masks.
PubMed: 38911991
DOI: 10.18103/mra.v12i5.5441 -
MicroPublication Biology 2024Standardizing image datasets is essential for facilitating overall visual comparisons and enhancing compatibility with image-processing workflows. One way to achieve...
Standardizing image datasets is essential for facilitating overall visual comparisons and enhancing compatibility with image-processing workflows. One way to achieve homogeneity for images containing a single object is to align the object to a common orientation. Here, we propose the Virtual Orientation Tools (VOTj): a set of Fiji plugins to center and align an object of interest in images to a vertical or horizontal orientation. To process an image, the plugin requires either a mask outlining the object or a rough annotation of the object directly drawn by the user in the image. The current object orientation is retrieved using Principal Component Analysis (PCA), from which the optimal alignment is derived. The plugins support multi-dimensional images to allow, e.g., aligning individual time points of a time-lapse. The tools can be used for a variety of samples and imaging modalities. Besides, the plugins enable the interactive alignment of a list of images from a directory for batch execution and can be included in custom image-processing workflows using macro-recording.
PubMed: 38911438
DOI: 10.17912/micropub.biology.001221 -
Cureus May 2024Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography...
Objective and background This study aimed to develop a deep convolutional neural network (DCNN) model capable of generating synthetic 4D magnetic resonance angiography (MRA) from 3D time-of-flight (TOF) images, allowing estimation of temporal changes in arterial flow. TOF MRA provides static information about arterial structures through maximum intensity projection (MIP) processing, but it does not capture the dynamic information of contrast agent circulation, which is lost during MIP processing. Considering the principles of TOF, it is hypothesized that dynamic information about arterial blood flow is latent within TOF signals. Although arterial spin labeling (ASL) can extract dynamic arterial information, ASL MRA has drawbacks, such as longer imaging times and lower spatial resolution than TOF MRA. This study's primary aim is to extend the utility of TOF MRA by training a machine-learning model on paired TOF and ASL data to extract latent dynamic information from TOF signals. Methods A DCNN combining a modified U-Net and a long-short-term memory (LSTM) network was trained on a dataset of 13 subjects (11 men and two women, aged 42-77 years) using paired 3D TOF MRA and 4D ASL MRA images. Subjects had no history of cerebral vessel occlusion or significant stenosis. The dataset was acquired using a 3T MRI system with a 32-channel head coil. Preprocessing involved resampling and intensity normalization of TOF and ASL images, followed by data augmentation and arterial mask generation. The model learned to extract flow information from TOF images and generate 8-phase 4D MRA images. The precision of flow estimation was evaluated using the coefficient of determination (R²) and Bland-Altman analysis. A board-certified neuroradiologist validated the quality of the images and the absence of significant stenosis in the major cerebral arteries. Results The generated 4D MRA images closely resembled the ground-truth ASL MRA data, with R² values of 0.92, 0.85, and 0.84 for the internal carotid artery (ICA), proximal middle cerebral artery (MCA), and distal MCA, respectively. Bland-Altman analysis revealed a systematic error of -0.06, with 95% agreement limits ranging from -0.18 to 0.12. Additionally, the model successfully identified flow abnormalities in a subject with left MCA stenosis, displaying a delayed peak and subsequent flattening distal to the stenosis, indicative of reduced blood flow. Visualization of the predicted arterial flow overlaid on the original TOF MRA images highlighted the spatial progression and dynamics of the flow. Conclusions The DCNN model effectively generated synthetic 4D MRA images from TOF images, demonstrating its potential to estimate temporal changes in arterial flow accurately. This non-invasive technique offers a promising alternative to conventional methods for visualizing and evaluating healthy and pathological flow dynamics. It has significant potential to improve the diagnosis and treatment of cerebrovascular diseases by providing detailed temporal flow information without the need for contrast agents or invasive procedures. The practical implementation of this model could enable the extraction of dynamic cerebral blood flow information from routine brain MRI examinations, contributing to the early diagnosis and management of cerebrovascular disorders.
PubMed: 38910733
DOI: 10.7759/cureus.60803 -
BioData Mining Jun 2024Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain...
Deep learning shows great promise for medical image analysis but often lacks explainability, hindering its adoption in healthcare. Attribution techniques that explain model reasoning can potentially increase trust in deep learning among clinical stakeholders. In the literature, much of the research on attribution in medical imaging focuses on visual inspection rather than statistical quantitative analysis.In this paper, we proposed an image-based saliency framework to enhance the explainability of deep learning models in medical image analysis. We use adaptive path-based gradient integration, gradient-free techniques, and class activation mapping along with its derivatives to attribute predictions from brain tumor MRI and COVID-19 chest X-ray datasets made by recent deep convolutional neural network models.The proposed framework integrates qualitative and statistical quantitative assessments, employing Accuracy Information Curves (AICs) and Softmax Information Curves (SICs) to measure the effectiveness of saliency methods in retaining critical image information and their correlation with model predictions. Visual inspections indicate that methods such as ScoreCAM, XRAI, GradCAM, and GradCAM++ consistently produce focused and clinically interpretable attribution maps. These methods highlighted possible biomarkers, exposed model biases, and offered insights into the links between input features and predictions, demonstrating their ability to elucidate model reasoning on these datasets. Empirical evaluations reveal that ScoreCAM and XRAI are particularly effective in retaining relevant image regions, as reflected in their higher AUC values. However, SICs highlight variability, with instances of random saliency masks outperforming established methods, emphasizing the need for combining visual and empirical metrics for a comprehensive evaluation.The results underscore the importance of selecting appropriate saliency methods for specific medical imaging tasks and suggest that combining qualitative and quantitative approaches can enhance the transparency, trustworthiness, and clinical adoption of deep learning models in healthcare. This study advances model explainability to increase trust in deep learning among healthcare stakeholders by revealing the rationale behind predictions. Future research should refine empirical metrics for stability and reliability, include more diverse imaging modalities, and focus on improving model explainability to support clinical decision-making.
PubMed: 38909228
DOI: 10.1186/s13040-024-00370-4 -
Journal of Environmental Management Jun 2024Selecting the optimal monitoring points in a water distribution network is challenging due to the complex spatiotemporal variability of water quality degradation. The...
Selecting the optimal monitoring points in a water distribution network is challenging due to the complex spatiotemporal variability of water quality degradation. The lack of a standardized methodology for monitoring point selection forces operators to rely on general recommendations, historical data and professional experience, which can mask water quality problems and increase the risk to consumers. This study proposes a new methodology to optimize the selection of monitoring points in distribution networks. The method considers the spatiotemporal degradation of water quality, the definition of representative zones and two selection criteria: global representativeness and potential health risk. Representative zones were determined for each node of the network based on hydraulic paths and their water quality spatial variability. Part of the distribution network in Quebec City, Canada was used as the case study, in which four water quality parameters were investigated: free chlorine residual (FRC), heterotrophic plate counts (HPC), trihalomethanes (THMs) and haloacetic acids (HAAs). Seasonal variabilities (summer and winter) were also analyzed. The results obtained for the two criteria and for both seasons were compared, and methodological and practical recommendations were established for dynamic monitoring programs that respond to the needs of operators.
PubMed: 38908156
DOI: 10.1016/j.jenvman.2024.121505 -
PLOS Global Public Health 2024Community Health Workers (CHWs) are a key human resource for health particularly in low- and middle-income countries. In many parts of the world, CHWs are known to have...
Community Health Workers (CHWs) are a key human resource for health particularly in low- and middle-income countries. In many parts of the world, CHWs are known to have played an instrumental role in controlling the COVID-19 pandemic. This study explored the involvement of CHWs in the COVID-19 response in Uganda. A qualitative study that involved 10 focus group discussions (FGDs) among CHWs was conducted. The study was carried out in 5 districts of Amuria, Karenga, Kamwenge, Bugiri and Pader. The FGD guide used explored the role of CHWs in the COVID-19 response in their communities including lived experiences, challenges, and coping mechanisms. The data were analyzed thematically with the support of NVivo version 12 pro (QSR International). CHWs were at the frontline of COVID-19 prevention interventions at households and in the community. CHWs raised awareness on prevention measures including wearing face masks, hand hygiene, and social distancing. They identified suspected cases such as new members entering the community, as well as individuals returning from abroad with signs and symptoms of COVID-19. CHWs mobilized the community and increased awareness on COVID-19 vaccination which played an important role in reducing misinformation. They also supported home-based management of mild COVID-19 cases through isolation of patients; provided health and nutritional guidance among patients in their homes; and referred suspected cases to health facilities for testing and management. Both monetary and non-monetary incentives were provided to support CHWs in the COVID-19 response. However, the adequacy and timing of the incentives were inadequate. Routine services of CHWs such as health promotion and treatment of childhood illnesses were disrupted during the pandemic. CHWs played an instrumental role in response to the pandemic especially on surveillance, risk communication, and observance of preventing measures. Strategies to ensure that routine services of CHWs are not disrupted during pandemics are needed.
PubMed: 38905244
DOI: 10.1371/journal.pgph.0003312 -
PloS One 2024Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19)...
Faces are a crucial environmental trigger. They communicate information about several key features, including identity. However, the 2019 coronavirus pandemic (COVID-19) significantly affected how we process faces. To prevent viral spread, many governments ordered citizens to wear masks in public. In this research, we focus on identifying individuals from images or videos by comparing facial features, identifying a person's biometrics, and reducing the weaknesses of person recognition technology, for example when a person does not look directly at the camera, the lighting is poor, or the person has effectively covered their face. Consequently, we propose a hybrid approach of detecting either a person with or without a mask, a person who covers large parts of their face, and a person based on their gait via deep and machine learning algorithms. The experimental results are excellent compared to the current face and gait detectors. We achieved success of between 97% and 100% in the detection of face and gait based on F1 score, precision, and recall. Compared to the baseline CNN system, our approach achieves extremely high recognition accuracy.
Topics: Humans; COVID-19; Neural Networks, Computer; Machine Learning; Deep Learning; Algorithms; SARS-CoV-2; Face; Gait; Biometric Identification
PubMed: 38905186
DOI: 10.1371/journal.pone.0300614